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BRYAN GUNER
PROFESSIONAL PORTFOLIO
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TABLE OF CONTENTS
Contact Information
Software Skills
Project Experience & Code
Samples
GitHub:
https://github.com/bgoonz
Contact Information:
Address: 150 Henley Place, Weehawken, NJ, 07086
LinkedIn: https://www.linkedin.com/in/bryan-guner-04619912 8/
Phone (mobile): 551-254-5505
Email(s):
bryan.guner@gmail.com
TECHNICAL
SKILLS &
SOFTWARE
DYNAMIC TIME WARPING TRIGGERED GUITAR EFFEC TS PLATFORM
System Architecture:
read in a guitar signal during the ‘learning’ phase and isolate
subsections of a performance needed to generate the DTW
learned-threshold.
then implement an analysis based on a modified dynamic time
warping (DTW) algorithm, to compare the DTW cost function of
incoming live audio ag ainst the cost-thresholds determined in
the pre recording phase.
In live performance, guitar effect pedals are a versatile yet limiting asset, requiring presence of mind on the part
of the performer. This platform offers an automatic solution to the r estrictions that guitar effect pedals pr esent.
Senior Design Project (T CNJ)
HOW IT WORKS:
This automation was ac hieved through the use
of Pure Data, a GUI for audio manipulation
applications, with embedded Python
externals. When the algorithm detects a
match, the platform r uns the ‘pure’ digitalized
audio signal through custom made PD effects
patches!
Dynamic Time Warping Exter nal:
Feed in two song performances for learning
phase and obtain Least Cost Path (LCP) for
each sub signal
Compare Incoming live signal with sub signal
of one of the recorded performance
When LCP value is less than or equal to the
LCP obtained from learning phase, trigger
guitar effect
PURE DATA PATCH DEMOS:
Recording (.wav) Patch: Reverb Patc h Spectral Delay Patch (visible part of)Fuzz Patch
Signal sent DI into Scarle t 2i2 to be
ported into pure data and then back
out the DAC into a Fender Super
Champ X2 FSR 15-watt 1x10" Tube
Combo Amp using the settings below
and was captured by my I phone's mic.
PRESS PLAY
PLATFORM DEMO (WAIT UNTIL 0:18 FOR AUDIO)
This demo uses the reverb patc h (please forgive the quality of my guitar performance in this; at the time of this
recording I hadn’t slept in almost 48 hours in a desperate attempt to meet multiple concurrent project deadlines!!!)
https://github.com/bgoonz/a-A-Cheat-Sheet.git
BEST PRACTICE & TOOLS & ANIMATED VSCO DE EXTENSION GUIDE
https://github.com/bgoonz/Javascript-Best-Practices_--Tools
https://github.com/bgoonz/useful-snippets-js.git
GUITAR DELAY PEDAL PROJECT
BRYAN GUNER
DESIGN ELEMENTS
Analog to Digital Converter (taking analog guitar input, converting to digital to read
and utilize)
Rectifier & Pre-Amplifier (to accommodate the input constraints of the micro controller)
Digital to Analog Converter (taking digital signal, converting to analog for output)
Finite Impulse Response Digital Filter
2 Push Buttons
OBJECTIVE
Design a guitar pedal to take a guitar signal as an input, and output the dela yed and
echoed sound numerous times
The pedal must implement a preamplifier in order to manag e the negative voltage
input
The pedal must poll f or updated input data in order to produce a continuous output
The pedal must use an ADC in order to read and utilize the input
CURRENT MODEL
Dual Op-Amp (MC1458)
Physical Implementation of Circuit Design
BLOCK DIAGRAM
Guitar
Code
A/D
Pre
Amp
D/A
x
x
Amplif
ier
PSOC
SCHEMATIC
TIMING DIAGRAM
CURRENT
MODEL CODE
MATLAB SIMULATION
MATLAB was used to debug and simulate the gui tar
delay pedal
Utilized FIFO (first in first out), del ay and echo
PURE DATA LOOPER
Features:
tap tempo
Limiter on both the input
and the output to
prevent intermodulation
distortion produced by
superimposed layers.
“Reverse” function for
fun!
BASIC OPERATION
The loop is stored in a table in Pd, which is
played repeatedly.
While this table is being played, the output
and the current input is also written to it.
If there is no input the loop copied on itself.
DESIGN
CONSIDERATIONS
Pure Data processes the audio in blocks of samples
(64 samples by default).
Any audio event occurring during a block will only
be taken into account at the beginning of the next
block.
The table’s length must thus be a multiple of the
block size (64 in my patch), so that the end of the
last block of the loop corresponds exactly to the
beginning of the first bloc k.
If the table’s length is not a multiple of the bloc k
size, audible pops and clicks will appear while
looping, and gradually turn into a very annoying
noise.
PURE DATA SAMPLER
purpose of this design assignment was to implement and simulate both state space and transfer function descriptions of a set of
linearized differential equations that model
Modeling and Discrete-Logic Control
of a Quadruple-Tank System
Bryan Guner
1
and Sean Fernandez
1
; Advisor: Dr. Ambrose Adegbege
1
1
Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ
The aforementioned quadruple tank system consists of four
water tanks and two pumps that form a two-input two-output system. The
intention is to control the level of the water in the bottom two tanks while the
pumps delivers water to the top two tanks that trickle down into the bottom
two via two outlets situated at the bottom of the top two tanks. The inputs are
the control voltages sent to the pumps and the outputs are the sensor voltages
corresponding to the water levels in the lower tanks. A mathematical model is
developed using a combination of the mass balance equation, Bernoulli’s
equation and the derived flow coefficient taking into account the
proportionality splitting in the pump system.
The system is first modeled by a set of nonlinear differential
equations which are then linearized about an operating point that is comprised
of a water level height argument and the voltage to the pumps.
Design/Methods
Conclusion
.
Results
The goal of this design assignment was to develop and
simulate a mathematical model of a quadruple-tanks system and
subsequently implement discrete-logic control using the LabVIEW
software package. The purpose was to exercise our knowledge of the
differential equations that represent such a system, and to convert
them into linearized state-space and transfer function descriptions that
could be more easily used to simulate said system in LabVIEW.
Nonlinear Model sub-VI:
LabVIEW Implementation:
Linearized State-Space Model sub-
VI:
Linearized Transfer Function Model
sub-VI:
Schematic of 4 Tank
System:
Linear Equations:Nonlinear
Equations:
State-Space Configuration:
Transfer Function Configuration:
Quadruple Tank-System Block Diagram:
Output Water Levels Front Panel:
Purpose was to implement and simulate both state
space and transfer function to model behavior of
system
Valuable learning experience in linearization of
discrete control of systems
Overall, students successfully modeled a quadruple-
tank system utilizing LabVIEW
Learned about the use of subVI’s to reduce
complexity and size of code
Close approximation to working experience of a
control systems engineer
Introduction
Abstract
XOR GATE USING CMOS LOGIC
XOR GATE BACKGROUND
Either A or B high will output
high
Both A and B high will output
low
Both A and B low will output low
PSPICE CMOS
CIRCUIT
DESIGN
SIMULATION RESULTS (ACTIVE PULL UP - RISING EDGE)
-- Delay to 50% about 40ns
SIMULATION RESULTS (ACTIVE PULL UP - FALLING
EDGE)
-Delay until level out
approximately .04us =
40ns
PSPICE RTL CIRCUIT DESIGN
SIMULATION RESULTS (RESISTIVE PULL UP RISING EDGE
1K)
SIMULATION RESULTS (RESISTIVE PULL UP FALLING
EDGE 1K)
SIMULATION RESULTS (RESISTIVE PULL UP RIS ING EDGE
100K)
SIMULATION RESULTS (RESISTIVE PULL UP F ALLING
EDGE 100K)
EXPERIMENTAL RESULTS (FALLING EDGE)
- Propagation delay between 50%-Input to 50%-Output
approximately 50ns
EXPERIMENTAL RESULTS (RISING EDGE)
- Propagation delay between 50%-Input to 50%-Output
approximately 50ns
SIMULATION RESULTS (RESISTIVE PULL UP RISING EDGE
1K)
SIMULATION RESULTS (RESISTIVE PULL UP F ALLING
EDGE 1K)
SIMULATION RESULTS (RESISTIVE PULL UP RIS ING EDGE
100K)
SIMULATION RESULTS (RESISTIVE PULL UP F ALLING
EDGE 100K)
ONE HANDED KEYBOARD
ENG 142 FUNDAMENTALS OF ENGINEERING TERM PROJECT ( A WARDED INNOVATIVE DESIGN)
Ergonomic placement of c haracters decided by result of character frequency
counter (written in Java) as a substitute for the conventional qwerty layout.
SIGNAL DENOISING USING
WAVELET TRANSFORMATION
BRYAN GUNER
IMAGE DENOISING
CONCEPT
Compared to other denoising techniques, wavelet a nd wavelet pac ket denoising
allows the user to retain features in the data
Data can be compressed by by setting seemingly unimportant wavelet/wav elet
packets coefficients to zer o and reconstructing data
Interval-dependent thresholds can be applied to denoise data with non-constant
variance since signal noise is not always uniform in time
METHODS
Localize features in your data to different scales
You can preserve important signal or image features while removing noise
The wavelet transform leads to a sparse representation for many real-world
signals and images
The wavelet transform concentrates signal and image features in a few
large-magnitude wavelet coef ficients
Wavelet coefficients which are small in value are typically noise and you can
"shrink" those coefficients or remove them without affecting the signal or
image quality. After you threshold the coef ficients, you reconstruct the data
using the inverse wavelet transform
MATLAB IMPLEMENTATION
NOISY SIGNAL SHOWS ORIGINAL SIGNAL WITH ADDED WHITE NOISE
MATLAB WAVELET DENOISING METHOD 1
Symlet Wavelet Family
(Symmetrical)
Universal Threshold
COEFFICIENT
GRAPH
Original coefficients
shown in blue
Denoised coefficients
shown in orange
Samples(X) Vs. Lev el(Y)
MATLAB WAVELET
DENOISING
METHOD 2
Symlet Wavelet Family
(Symmetrical)
SURE Method
COEFFICIENT
GRAPH
Original coefficients
shown in blue
Denoised coefficients
shown in orange
Samples(X) Vs. Lev el(Y)
MATLAB WAVELET
DENOISING
METHOD 3
Symlet Wavelet Family
(Symmetrical)
Minimax Method
COEFFICIENT
GRAPH
Original coefficients
shown in blue
Denoised coefficients
shown in orange
Samples(X) Vs. Lev el(Y)